Rxivist logo

TiTUS: Sampling and Summarizing Transmission Trees with Multi-strain Infections

By Palash Sashittal, Mohammed El-Kebir

Posted 18 Mar 2020
bioRxiv DOI: 10.1101/2020.03.17.996041

Motivation The combination of genomic and epidemiological data hold the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. Results We formulate the Direct Transmission Inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce TiTUS, a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritizes parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUS’s ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. Availability <https://github.com/elkebir-group/TiTUS> Contact melkebir{at}illinois.edu Supplementary information Supplementary data are available at Bioinformatics online.

Download data

  • Downloaded 183 times
  • Download rankings, all-time:
    • Site-wide: 83,679 out of 103,703
    • In bioinformatics: 8,178 out of 9,474
  • Year to date:
    • Site-wide: 35,823 out of 103,703
  • Since beginning of last month:
    • Site-wide: 76,127 out of 103,703

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)